Descriptives of Duration of Activities
Janet Bang
November 17, 2022
This code is for the duration-based activity information.
Load libraries and set theme
library(tidyverse)## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.0 ──## ✔ ggplot2 3.3.6 ✔ purrr 0.3.4
## ✔ tibble 3.1.0 ✔ dplyr 1.0.5
## ✔ tidyr 1.1.3 ✔ stringr 1.4.0
## ✔ readr 1.3.1 ✔ forcats 0.5.0## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()library(gridExtra)##
## Attaching package: 'gridExtra'## The following object is masked from 'package:dplyr':
##
## combinelibrary(psych)##
## Attaching package: 'psych'## The following objects are masked from 'package:ggplot2':
##
## %+%, alphatheme_set(theme_bw())Prep data
Read in data and demographic information
data_dur <- read_csv("./data_demo_lena_transcripts/elan_activity_dur.csv") %>%
mutate(id = factor(id),
language = factor(language)) %>%
dplyr::select(-X1) %>%
rename(tcds_min_seg = tcds_min) %>%
mutate(activity = factor(activity, levels = c("books", "play", "food", "routines", "conv", "ac", "non-tcds")))## Warning: Missing column names filled in: 'X1' [1]## Parsed with column specification:
## cols(
## X1 = col_double(),
## activity = col_character(),
## id = col_double(),
## rectime = col_double(),
## segment_num = col_double(),
## language = col_character(),
## dur_min = col_double(),
## Dur10minval = col_double(),
## AWCval = col_double(),
## Time = col_double(),
## Date = col_character(),
## dur_10min = col_double(),
## tcds_min = col_double()
## )str(data_dur)## tibble[,12] [1,654 × 12] (S3: tbl_df/tbl/data.frame)
## $ activity : Factor w/ 7 levels "books","play",..: 2 6 5 6 5 4 4 6 5 2 ...
## $ id : Factor w/ 90 levels "7292","7352",..: 46 46 46 46 46 46 46 46 46 46 ...
## $ rectime : num [1:1654] 15242 15242 15242 14342 14342 ...
## $ segment_num : num [1:1654] 2 2 2 3 3 3 4 4 4 5 ...
## $ language : Factor w/ 2 levels "english","spanish": 1 1 1 1 1 1 1 1 1 1 ...
## $ dur_min : num [1:1654] 3.05 3.38 2.51 6.3 2.03 ...
## $ Dur10minval : num [1:1654] 600 600 600 600 600 600 512 512 512 538 ...
## $ AWCval : num [1:1654] 728 728 728 681 681 681 602 602 602 596 ...
## $ Time : num [1:1654] 20.4 20.4 20.4 20.2 20.2 ...
## $ Date : chr [1:1654] "2010.10.18" "2010.10.18" "2010.10.18" "2010.10.18" ...
## $ dur_10min : num [1:1654] 10 10 10 10 10 ...
## $ tcds_min_seg: num [1:1654] 8.95 8.95 8.95 8.66 8.66 ...# create two dfs for plots
data_dur_en <- data_dur %>% filter(language == "english")
data_dur_sp <- data_dur %>% filter(language == "spanish")Check number of segments per participant
# checking to see there are 6 segments per participant
num_segments_english <- data_dur_en %>%
group_by(id, segment_num) %>%
distinct(segment_num) %>%
ungroup() %>%
count(id)
arrange(num_segments_english, n)## # A tibble: 45 x 2
## id n
## <fct> <int>
## 1 20001 6
## 2 20003 6
## 3 20004 6
## 4 20007 6
## 5 20034 6
## 6 20041 6
## 7 20048 6
## 8 20050 6
## 9 20056 6
## 10 20071 6
## # … with 35 more rowsnum_segments_spanish <- data_dur_sp %>%
group_by(id, segment_num) %>%
distinct(segment_num) %>%
ungroup() %>%
count(id)
arrange(num_segments_spanish, n)## # A tibble: 45 x 2
## id n
## <fct> <int>
## 1 7292 6
## 2 7352 6
## 3 7355 6
## 4 7363 6
## 5 7373 6
## 6 7409 6
## 7 7412 6
## 8 7433 6
## 9 7446 6
## 10 7448 6
## # … with 35 more rowsProportion duration - RAW per 10 min
# duration - minutes
ggplot(data_dur, aes(activity, dur_min, fill = activity)) +
theme_classic() +
geom_boxplot() +
geom_jitter(alpha = .3) +
scale_fill_manual(values=c("darkviolet", "firebrick1", "green2", "dodgerblue1", "darkgoldenrod1", "darkgrey", "black")) +
facet_wrap(~ language) +
theme(legend.position= "none") +
# theme(text = element_text(size = 35)) +
theme(axis.text.x = element_text(angle = 20, hjust = .7),
text = element_text(size = 30)) +
labs(x = "Activity", y = "Duration (min)") +
theme(panel.spacing = unit(4, "lines"))
ggsave("./figures/boxplot_duration.pdf", dpi = 300, width = 18, height = 8, units = "in")
# summarize data per participant and activity (all speech only)
# english
data_en_act <- data_dur_en %>%
group_by(id, activity) %>%
mutate(dur_min_act = mean(dur_min)) %>%
distinct(id, language, activity, dur_min_act) %>%
mutate(activity = factor(activity, levels = c("books", "play", "food", "routines", "conv", "ac", "non-tcds")))
# see min and max of actual data
data_dur_en %>%
ungroup() %>%
group_by(id, activity) %>%
mutate(min = min(dur_min),
max = max(dur_min)) %>%
distinct(activity, min, max)## # A tibble: 257 x 4
## # Groups: id, activity [257]
## activity id min max
## <fct> <fct> <dbl> <dbl>
## 1 play 20001 3.05 6.86
## 2 ac 20001 0.889 6.30
## 3 conv 20001 0.246 3.56
## 4 routines 20001 0.337 2.38
## 5 play 20003 2.54 2.90
## 6 routines 20003 5.61 7.81
## 7 ac 20003 0.0188 1.45
## 8 books 20003 3.55 6.57
## 9 food 20003 0.0118 3.31
## 10 play 20004 0.238 3.68
## # … with 247 more rows# spanish
# note that min and max are of averages, not of actual data
data_sp_act <- data_dur_sp %>%
group_by(id, activity) %>%
mutate(dur_min_act = mean(dur_min)) %>%
distinct(id, language, activity, dur_min_act) %>%
mutate(activity = factor(activity, levels = c("books", "play", "food", "routines", "conv", "ac", "non-tcds")))
# see min and max of actual data
data_dur_sp %>%
ungroup() %>%
group_by(activity) %>%
mutate(min = min(dur_min),
max = max(dur_min)) %>%
distinct(activity, min, max)## # A tibble: 7 x 3
## # Groups: activity [7]
## activity min max
## <fct> <dbl> <dbl>
## 1 ac 0.00407 9.93
## 2 play 0.140 9.97
## 3 conv 0.100 10
## 4 food 0.421 9.99
## 5 routines 0.0818 10
## 6 books 1.15 10
## 7 non-tcds 0.00368 8.98# descriptives
# note that min and max are of averages, not of actual data
describeBy(data_en_act$dur_min_act, data_en_act$activity, mat = T, fast = T)## item group1 vars n mean sd min max range
## X11 1 books 1 22 4.898482 2.018585 1.845591667 8.026042 6.180450
## X12 2 play 1 39 3.854464 1.728633 0.817550000 7.480950 6.663400
## X13 3 food 1 31 2.772894 2.225333 0.062700000 9.857617 9.794917
## X14 4 routines 1 32 2.182612 1.587954 0.044533333 6.448958 6.404425
## X15 5 conv 1 43 2.370739 1.522432 0.109333333 6.675067 6.565733
## X16 6 ac 1 45 1.797001 1.165076 0.007483333 4.802520 4.795037
## X17 7 non-tcds 1 45 3.655110 1.631661 0.880413889 7.118992 6.238578
## se
## X11 0.4303637
## X12 0.2768027
## X13 0.3996816
## X14 0.2807133
## X15 0.2321687
## X16 0.1736792
## X17 0.2432337describeBy(data_sp_act$dur_min_act, data_sp_act$activity, mat = T, fast = T)## item group1 vars n mean sd min max range
## X11 1 books 1 20 6.182757 2.219610 1.14918333 8.936558 7.787375
## X12 2 play 1 37 3.806485 2.551134 0.21073333 9.810133 9.599400
## X13 3 food 1 31 4.084679 2.588315 0.49641667 9.967417 9.471000
## X14 4 routines 1 35 2.849555 1.753518 0.15326667 7.072858 6.919592
## X15 5 conv 1 43 2.829679 1.963876 0.16178333 8.328506 8.166722
## X16 6 ac 1 45 2.721287 1.434980 0.44101250 6.044525 5.603512
## X17 7 non-tcds 1 45 3.206051 1.717209 0.08946667 7.312114 7.222647
## se
## X11 0.4963199
## X12 0.4194038
## X13 0.4648751
## X14 0.2963986
## X15 0.2994883
## X16 0.2139141
## X17 0.2559864Proportion duration - summed across segments
# duration - minutes
data_dur_sum <- data_dur %>%
group_by(id, activity) %>%
mutate(dur_act_total = sum(dur_min)) %>%
distinct(id, language, activity, dur_act_total) %>%
ungroup() %>%
group_by(id) %>%
mutate(dur_hour = sum(dur_act_total))
ggplot(data_dur_sum, aes(activity, dur_act_total, fill = activity)) +
theme_classic() +
geom_boxplot() +
geom_jitter(alpha = .3) +
scale_fill_manual(values=c("darkviolet", "firebrick1", "green2", "dodgerblue1", "darkgoldenrod1", "darkgrey", "black")) +
facet_wrap(~ language) +
theme(legend.position= "none") +
# theme(text = element_text(size = 35)) +
theme(axis.text.x = element_text(angle = 20, hjust = .7),
text = element_text(size = 30)) +
labs(x = "Activity", y = "Sum Duration Across Segments (min)") +
theme(panel.spacing = unit(4, "lines"))
data_dur_sum_en <- data_dur_sum %>% filter(language == "english")
data_dur_sum_sp <- data_dur_sum %>% filter(language == "spanish")
# descriptives
# note that min and max are of averages, not of actual data
describeBy(data_dur_sum_en$dur_act_total, data_dur_sum_en$activity, mat = T, fast = T)## item group1 vars n mean sd min max range
## X11 1 books 1 22 11.452584 9.196898 2.089166667 39.92383 37.83467
## X12 2 play 1 39 10.512915 7.987952 1.101933333 33.71398 32.61205
## X13 3 food 1 31 5.786824 5.328664 0.062700000 20.17702 20.11432
## X14 4 routines 1 32 4.840254 5.070568 0.044533333 25.63767 25.59313
## X15 5 conv 1 43 8.031319 6.026471 0.109333333 24.31743 24.20810
## X16 6 ac 1 45 7.800962 6.645371 0.007483333 25.96090 25.95342
## X17 7 non-tcds 1 45 21.854143 9.813560 5.282483333 42.71395 37.43147
## se
## X11 1.9607853
## X12 1.2790960
## X13 0.9570564
## X14 0.8963582
## X15 0.9190282
## X16 0.9906334
## X17 1.4629191describeBy(data_dur_sum_sp$dur_act_total, data_dur_sum_sp$activity, mat = T, fast = T)## item group1 vars n mean sd min max range
## X11 1 books 1 20 12.162309 10.363463 1.1491833 45.30968 44.16050
## X12 2 play 1 37 8.598127 7.822008 0.2107333 27.69682 27.48608
## X13 3 food 1 31 7.321418 5.671825 0.4964167 20.78387 20.28745
## X14 4 routines 1 35 4.911878 3.879373 0.1532667 14.14572 13.99245
## X15 5 conv 1 43 8.231171 6.919189 0.1617833 24.98552 24.82373
## X16 6 ac 1 45 11.152151 7.595329 1.0423667 30.58165 29.53928
## X17 7 non-tcds 1 45 19.189398 10.347160 0.3578667 43.87268 43.51482
## se
## X11 2.3173409
## X12 1.2859302
## X13 1.0186899
## X14 0.6557337
## X15 1.0551665
## X16 1.1322447
## X17 1.5424636Proportion duration - other child-centered
# duration - minutes
data_dur_othercc <- data_dur %>%
mutate(activity2 = ifelse(activity == "books", "books",
ifelse(activity == "ac", "ac",
ifelse(activity == "non-tcds", "non-tcds", "othercc")))) %>%
group_by(id, activity2) %>%
mutate(dur_act_total = sum(dur_min)) %>%
distinct(id, language, activity2, dur_act_total) %>%
ungroup() %>%
group_by(id) %>%
mutate(dur_hour = sum(dur_act_total))
data_dur_othercc_en <- data_dur_othercc %>% filter(language == "english")
data_dur_othercc_sp <- data_dur_othercc %>% filter(language == "spanish")
# descriptives
# note that min and max are of averages, not of actual data
describeBy(data_dur_othercc_en$dur_act_total, data_dur_othercc_en$activity2, mat = T, fast = T)## item group1 vars n mean sd min max range
## X11 1 ac 1 45 7.800962 6.645371 0.007483333 25.96090 25.95342
## X12 2 books 1 22 11.452584 9.196898 2.089166667 39.92383 37.83467
## X13 3 non-tcds 1 45 21.854143 9.813560 5.282483333 42.71395 37.43147
## X14 4 othercc 1 45 24.214002 11.451546 1.384766667 51.27485 49.89008
## se
## X11 0.9906334
## X12 1.9607853
## X13 1.4629191
## X14 1.7070957describeBy(data_dur_othercc_sp$dur_act_total, data_dur_othercc_sp$activity2, mat = T, fast = T)## item group1 vars n mean sd min max range
## X11 1 ac 1 45 11.15215 7.595329 1.0423667 30.58165 29.53928
## X12 2 books 1 20 12.16231 10.363463 1.1491833 45.30968 44.16050
## X13 3 non-tcds 1 45 19.18940 10.347160 0.3578667 43.87268 43.51482
## X14 4 othercc 1 45 23.79891 11.905835 0.4964167 47.55308 47.05667
## se
## X11 1.132245
## X12 2.317341
## X13 1.542464
## X14 1.774817Plotting activity by time of day
For publication
data_dur_pub <- data_dur %>%
mutate(activity = recode(activity, "ac" = "Adult-cent.", "conv" = "Unst. Conv.",
"books" = "Books", "play" = "Playing", "food" = "Feeding",
"routines" = "Routines", "non-tcds" = "non-tCDS"),
language = recode(language, "english" = "English", "spanish" = "Spanish")) %>%
rename("Language" = "language") %>%
mutate(activity = factor(activity, levels = c("Books", "Playing", "Feeding", "Routines", "Unst. Conv.", "Adult-cent.", "non-tCDS")))
# combined groups
ggplot(data_dur_pub, aes(Time, fct_rev(activity), color = activity, shape = Language)) +
geom_jitter(size = 10, alpha = .5, stroke = 2) +
scale_x_continuous(breaks = c(5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24)) +
scale_color_manual(values = c("darkviolet", "firebrick1", "green2", "dodgerblue1", "darkgoldenrod1", "darkgrey", "lightgrey"), guide = "none") +
scale_shape_manual(values=c(16, 21)) +
geom_vline(xintercept = 8) +
geom_vline(xintercept = 12) +
geom_vline(xintercept = 16) +
geom_vline(xintercept = 20) +
theme(text = element_text(size = 40)) +
theme(legend.position = c(1, 1),
legend.justification = c(1, 1),
legend.box.margin=margin(c(10,10,10,10)),
legend.background = element_rect(fill = "white"), # , color = "black"
legend.text = element_text(size = 18),
legend.title = element_text(size = 21),
legend.key.size = unit(1, "cm")) +
labs(x = "Time", y = "")
# theme(axis.title.y = element_text(angle = 0, vjust = 0.5))
ggsave("./figures/time_activity_by_dur.pdf", width = 22, height = 11, dpi = 300)Pie charts for proportion duration of time per hour across families (adding time across six segments)
Create dfs for individual participants
# create duration per hour
total_dur_hour <- data_dur %>%
distinct(id, segment_num, rectime, Dur10minval, language) %>%
group_by(id) %>%
mutate(dur_six_segments = sum(Dur10minval)/60) %>%
distinct(id, dur_six_segments, language)
# merge with summary of dur per activity in one hour
variables_dur_all <- data_dur %>%
filter(activity != "non-tcds") %>%
dplyr::select(-c(dur_10min, tcds_min_seg)) %>%
full_join(total_dur_hour, by = c("id", "language")) %>%
group_by(id, activity) %>%
mutate(dur_activity_1hr = sum(dur_min)) %>%
distinct(id, activity, dur_activity_1hr, dur_six_segments, language)
# make wide, add zeros, make long, create proportion variable
data_dur_prop_all <- variables_dur_all %>%
spread(activity, dur_activity_1hr) %>%
replace_na(list(play = 0, food = 0, conv = 0, books = 0, routines = 0, ac = 0)) %>%
mutate(nontcds = dur_six_segments - (play + food + conv + books + routines + ac)) %>%
gather(activity, value = dur_activity_1hr_zeros,
play, food, conv, books, routines, ac, nontcds) %>%
mutate(prop_dur_activity_1hr = dur_activity_1hr_zeros / dur_six_segments) %>%
mutate(activity = factor(activity))
# create wide df for min per activity
data_dur_min_all_wide <- data_dur_prop_all %>%
dplyr::select(-prop_dur_activity_1hr) %>%
spread(activity, dur_activity_1hr_zeros) %>%
mutate(dur_min_total = ac + books + conv + food + nontcds + play + routines) %>%
mutate(dur_min_childcc = books + conv + food + play + routines) %>%
mutate(dur_min_tcds = ac + books + conv + food + play + routines)
psych::describeBy(data_dur_min_all_wide, data_dur_min_all_wide$language, fast = T)## Warning in FUN(newX[, i], ...): no non-missing arguments to min; returning Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to min; returning Inf## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf## Warning in FUN(newX[, i], ...): no non-missing arguments to min; returning Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to min; returning Inf## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf##
## Descriptive statistics by group
## group: english
## vars n mean sd min max range se
## id 1 45 NaN NA Inf -Inf -Inf NA
## language 2 45 NaN NA Inf -Inf -Inf NA
## dur_six_segments 3 45 59.47 1.09 55.23 60.00 4.77 0.16
## ac 4 45 7.80 6.65 0.01 25.96 25.95 0.99
## books 5 45 5.60 8.60 0.00 39.92 39.92 1.28
## conv 6 45 7.67 6.12 0.00 24.32 24.32 0.91
## food 7 45 3.99 5.17 0.00 20.18 20.18 0.77
## nontcds 8 45 21.85 9.81 5.28 42.71 37.43 1.46
## play 9 45 9.11 8.26 0.00 33.71 33.71 1.23
## routines 10 45 3.44 4.80 0.00 25.64 25.64 0.72
## dur_min_total 11 45 59.47 1.09 55.23 60.00 4.77 0.16
## dur_min_childcc 12 45 29.81 12.75 1.38 54.71 53.33 1.90
## dur_min_tcds 13 45 37.61 9.66 17.29 54.72 37.43 1.44
## ------------------------------------------------------------
## group: spanish
## vars n mean sd min max range se
## id 1 45 NaN NA Inf -Inf -Inf NA
## language 2 45 NaN NA Inf -Inf -Inf NA
## dur_six_segments 3 45 59.55 1.20 53.55 60.00 6.45 0.18
## ac 4 45 11.15 7.60 1.04 30.58 29.54 1.13
## books 5 45 5.41 9.15 0.00 45.31 45.31 1.36
## conv 6 45 7.87 6.97 0.00 24.99 24.99 1.04
## food 7 45 5.04 5.80 0.00 20.78 20.78 0.87
## nontcds 8 45 19.19 10.35 0.36 43.87 43.51 1.54
## play 9 45 7.07 7.82 0.00 27.70 27.70 1.17
## routines 10 45 3.82 3.99 0.00 14.15 14.15 0.59
## dur_min_total 11 45 59.55 1.20 53.55 60.00 6.45 0.18
## dur_min_childcc 12 45 29.20 14.16 0.50 52.37 51.87 2.11
## dur_min_tcds 13 45 40.36 10.69 16.13 59.64 43.51 1.59# create wide df for prop min per activity
data_dur_prop_all_wide <- data_dur_prop_all %>%
dplyr::select(-dur_activity_1hr_zeros) %>%
spread(activity, prop_dur_activity_1hr) %>%
mutate(prop_total = ac + books + conv + food + nontcds + play + routines) %>%
mutate(prop_childcc = books + conv + food + play + routines) %>%
mutate(prop_tcds = ac + books + conv + food + play + routines)
# descriptives for proportion of min per activity
psych::describeBy(data_dur_prop_all_wide, data_dur_prop_all_wide$language, fast = T)## Warning in FUN(newX[, i], ...): no non-missing arguments to min; returning Inf## Warning in FUN(newX[, i], ...): no non-missing arguments to min; returning Inf## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf## Warning in FUN(newX[, i], ...): no non-missing arguments to min; returning Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to min; returning Inf## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf##
## Descriptive statistics by group
## group: english
## vars n mean sd min max range se
## id 1 45 NaN NA Inf -Inf -Inf NA
## language 2 45 NaN NA Inf -Inf -Inf NA
## dur_six_segments 3 45 59.47 1.09 55.23 60.00 4.77 0.16
## ac 4 45 0.13 0.11 0.00 0.44 0.44 0.02
## books 5 45 0.09 0.14 0.00 0.67 0.67 0.02
## conv 6 45 0.13 0.10 0.00 0.41 0.41 0.02
## food 7 45 0.07 0.09 0.00 0.34 0.34 0.01
## nontcds 8 45 0.37 0.16 0.09 0.71 0.62 0.02
## play 9 45 0.15 0.14 0.00 0.59 0.59 0.02
## routines 10 45 0.06 0.08 0.00 0.46 0.46 0.01
## prop_total 11 45 1.00 0.00 1.00 1.00 0.00 0.00
## prop_childcc 12 45 0.50 0.21 0.02 0.91 0.89 0.03
## prop_tcds 13 45 0.63 0.16 0.29 0.91 0.62 0.02
## ------------------------------------------------------------
## group: spanish
## vars n mean sd min max range se
## id 1 45 NaN NA Inf -Inf -Inf NA
## language 2 45 NaN NA Inf -Inf -Inf NA
## dur_six_segments 3 45 59.55 1.20 53.55 60.00 6.45 0.18
## ac 4 45 0.19 0.13 0.02 0.51 0.49 0.02
## books 5 45 0.09 0.15 0.00 0.76 0.76 0.02
## conv 6 45 0.13 0.12 0.00 0.42 0.42 0.02
## food 7 45 0.08 0.10 0.00 0.35 0.35 0.01
## nontcds 8 45 0.32 0.18 0.01 0.73 0.73 0.03
## play 9 45 0.12 0.13 0.00 0.46 0.46 0.02
## routines 10 45 0.06 0.07 0.00 0.24 0.24 0.01
## prop_total 11 45 1.00 0.00 1.00 1.00 0.00 0.00
## prop_childcc 12 45 0.49 0.24 0.01 0.90 0.90 0.04
## prop_tcds 13 45 0.68 0.18 0.27 0.99 0.73 0.03Create dfs for single mean values across families
prop_dur_mean_1hr_all <- data_dur_prop_all %>%
group_by(activity, language) %>%
dplyr::summarize(mean = mean(prop_dur_activity_1hr, na.rm=TRUE)) %>%
ungroup() %>%
mutate(activity = factor(activity, levels = c("books", "play", "food", "routines", "conv", "ac", "non-tcds")))## `summarise()` has grouped output by 'activity'. You can override using the
## `.groups` argument.prop_dur_mean_1hr_all %>% spread(activity, mean)## # A tibble: 2 x 8
## language books play food routines conv ac `<NA>`
## <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 english 0.0939 0.153 0.0671 0.0583 0.129 0.131 0.367
## 2 spanish 0.0906 0.118 0.0845 0.0641 0.132 0.188 0.323For publication
prop_dur_mean_1hr_all_pub <- prop_dur_mean_1hr_all %>%
mutate(language = recode(language, "english" = "English", "spanish" = "Spanish")) %>%
mutate(activity = recode(activity, "ac" = "adult-cent.", "conv" = "unst. conv.", "nontcds" = "non-tcds"))
ggplot(prop_dur_mean_1hr_all_pub, aes(x="", y = mean, fill = activity)) +
theme_classic() +
geom_bar(width = 1, stat = "identity") +
coord_polar("y", start = 0) +
scale_fill_manual(values=c("darkviolet", "firebrick1", "green2", "dodgerblue1", "darkgoldenrod1", "darkgrey", "grey92")) +
geom_text(aes(label = paste0(round(mean*100), "%")), position = position_stack(vjust = 0.5), size = 20) +
theme(axis.line = element_blank(),
axis.text = element_blank(),
axis.ticks = element_blank()) +
# scale_y_continuous(breaks = y.breaks, # where to place the labels
# labels = prop_dur_mean_1hr_all_pub$activity) + # the labels
labs(title = "", x = "", y = "") +
theme(text = element_text(size = 60),
legend.position = "none") +
# theme(legend.direction = "horizontal",
# legend.position = "bottom") +
facet_wrap(~ language)
ggsave("./figures/piechart_dur_1hr.pdf", width = 24, height = 24, units = "in", dpi = 300)Additional figures (not in publication)
Bar plot of each family and proportion duration per hour - sorted by AWC in one hour
# total awc per hour
total_awc_hour <- data_dur %>%
distinct(id, segment_num, rectime, AWCval, language) %>%
group_by(id) %>%
mutate(awc_total_tophr = sum(AWCval)) %>%
distinct(id, awc_total_tophr, language)
data_dur_prop_en <- data_dur_prop_all %>%
full_join(total_awc_hour, by = c("id", "language")) %>%
filter(language == "english") %>%
mutate(activity = factor(activity, levels = c("books", "play", "food", "routines", "conv", "ac", "nontcds")))
data_dur_prop_sp <- data_dur_prop_all %>%
full_join(total_awc_hour, by = c("id", "language")) %>%
filter(language == "spanish") %>%
mutate(activity = factor(activity, levels = c("books", "play", "food", "routines", "conv", "ac", "nontcds")))
# plots
ggplot(data_dur_prop_en, aes(x = reorder(id, awc_total_tophr), y = prop_dur_activity_1hr, fill = activity)) +
geom_col() +
scale_fill_manual(values=c("darkviolet", "firebrick1", "dodgerblue1", "green2", "darkgoldenrod1", "darkgrey", "black")) +
theme(axis.text.x = element_text(angle = 90, hjust = 1))
ggplot(data_dur_prop_sp, aes(x = reorder(id, awc_total_tophr), y = prop_dur_activity_1hr, fill = activity)) +
geom_col() +
scale_fill_manual(values=c("darkviolet", "firebrick1", "dodgerblue1", "green2", "darkgoldenrod1", "darkgrey", "black")) +
theme(axis.text.x = element_text(angle = 90, hjust = 1))
# plots - highlighting book reading
ggplot(data_dur_prop_en, aes(x = reorder(id, awc_total_tophr), y = prop_dur_activity_1hr, fill = activity)) +
geom_col() +
scale_fill_manual(values=c("darkviolet", "grey", "grey", "grey", "grey", "grey", "black")) +
theme(axis.text.x = element_text(angle = 90, hjust = 1))
ggplot(data_dur_prop_sp, aes(x = reorder(id, awc_total_tophr), y = prop_dur_activity_1hr, fill = activity)) +
geom_col() +
scale_fill_manual(values=c("darkviolet", "grey", "grey", "grey", "grey", "grey", "black")) +
theme(axis.text.x = element_text(angle = 90, hjust = 1))
Instance of each activity per participant
ggplot(data_dur_en, aes(id, fct_rev(activity), color = activity)) +
geom_point(size = 8) +
scale_color_manual(values=c("darkviolet", "firebrick1", "dodgerblue1", "green2", "darkgoldenrod1", "darkgrey", "black")) +
theme(axis.text.x = element_text(angle = 90, hjust = 1))
ggplot(data_dur_sp, aes(id, fct_rev(activity), color = activity)) +
geom_point(size = 8) +
scale_color_manual(values=c("darkviolet", "firebrick1", "dodgerblue1", "green2", "darkgoldenrod1", "darkgrey", "black")) +
theme(axis.text.x = element_text(angle = 90, hjust = 1))